The central analytic focus of most policy-oriented social research today is the assessment of the relative importance of competing independent variables in multivariate analyses. A researcher might ...
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The central analytic focus of most policy-oriented social research today is the assessment of the relative importance of competing independent variables in multivariate analyses. A researcher might ask: “Which variable has the strongest impact on life chances: education, test scores, or family background?” In this book we offer an alternative to the conventional approach to the analysis of policy-relevant social data. Instead of asking, "What is the net effect of each independent variable on the outcome?" we ask, "What combinations of causally relevant conditions are consistently linked to the outcome?" Thus, in our approach causal conditions do not compete with each other; rather, they combine in different ways to produce the outcome. This alternate approach, which utilizes set-analytic techniques, allows for the possibility that there may be several paths to the same outcome, which in turn may differ by race and gender. We illustrate this new approach via a re-analysis of the Bell Curve data. We show that by viewing cases intersectionally and causes conjuncturally, researchers can address nuanced questions about the causal conditions linked to poverty. Our central findings demonstrate dramatic racial differences in the connections between advantages versus disadvantages and the experience versus the avoidance of poverty.Less

Intersectional Inequality : Race, Class, Test Scores, and Poverty

Charles C. RaginPeer C. Fiss

Published in print: 2016-12-20

The central analytic focus of most policy-oriented social research today is the assessment of the relative importance of competing independent variables in multivariate analyses. A researcher might ask: “Which variable has the strongest impact on life chances: education, test scores, or family background?” In this book we offer an alternative to the conventional approach to the analysis of policy-relevant social data. Instead of asking, "What is the net effect of each independent variable on the outcome?" we ask, "What combinations of causally relevant conditions are consistently linked to the outcome?" Thus, in our approach causal conditions do not compete with each other; rather, they combine in different ways to produce the outcome. This alternate approach, which utilizes set-analytic techniques, allows for the possibility that there may be several paths to the same outcome, which in turn may differ by race and gender. We illustrate this new approach via a re-analysis of the Bell Curve data. We show that by viewing cases intersectionally and causes conjuncturally, researchers can address nuanced questions about the causal conditions linked to poverty. Our central findings demonstrate dramatic racial differences in the connections between advantages versus disadvantages and the experience versus the avoidance of poverty.

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